scholarly journals Automated Optimization of Intersections Using a Genetic Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15452-15468 ◽  
Author(s):  
Luis Cruz-Piris ◽  
Miguel A. Lopez-Carmona ◽  
Ivan Marsa-Maestre
Keyword(s):  
Genetic Algorithm ◽  
Automated Optimization

scholarly journals Automated Optimization for the Production Scheduling of Prefabricated Elements Based on the Genetic Algorithm and IFC Object Segmentation

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1593
Author(s):  
Zhao Xu ◽  
Xiang Wang ◽  
Zezhi Rao
Keyword(s):  
Genetic Algorithm ◽  
Production Process ◽  
Production Scheduling ◽  
Process Model ◽  
Query Language ◽  
Optimal Schedule ◽  
Optimization Framework ◽  
Computational Environment ◽  
Automated Optimization ◽  
Component Production

Background: With the ever-increasing availability of data and a higher level of automation and simulation, production scheduling in the factory for prefabrication can no longer be seen as an autonomous solution. Concepts such as building information modelling (BIM), graphic techniques, databases, and interface development as well as heightened emphasis on overall-process optimization topics increase the pressure to connect to and interact with interrelated tasks and procedures. Methods: The automated optimization framework detailed in this study intended to generate optimal schedule of prefabricated component production based on the manufacturing process model and genetic algorithm method. An extraction and segmentation approach based on industry foundation classes (IFC) for prefabricated component production is discussed. During this process, the position and geometric information of the prefabricated components are adjusted and output in the extracted IFC file. Then, the production process and the completion time of each process have been examined and simulated with the genetic algorithm. Lastly, the automated optimization solution can be formed by the linking production scheduling database and the computational environment. Results: This shows that the implementation of the automated optimization framework for the production scheduling of the prefabricated elements improves the operability and accuracy of the production process. Conclusions: Based on the integration technique discussed above, the data transmission and integration in the mating application program is achieved by linking the Python-based application, the Structured Query Language (SQL) database and the computational environment. The implementation of the automated optimization framework model enables BIM models to play a better foundational role in patching up the technical gaps between prefabricated building designers and element producers.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy
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